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- Tool Used Python Jupyter Notebook
- Dataset: http://bit.ly/w-data
- Problem Statement : What will be predicted score if a student studies for 9.25 hrs/ day?
- Here is the solution :
- Code : https://github.com/ManjiriSDS/The-Spark-Foundation/blob/main/Task%201%20The%20Sparks%20Foundation.ipynb
- Recorded Output in a Video :
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- Dataset: https://bit.ly/3kXTdox
- Here is the solution :
- Code : https://github.com/ManjiriSDS/The-Spark-Foundation/blob/main/Task%202%20The%20Sparks%20Foundation.ipynb
- Recorded Output in a Video : https://www.youtube.com/watch?v=Tk7fWt_TNcs&t=2s
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- Dataset: https://bit.ly/3kXTdox
- Problem Statement : If we feed any new data to this classifier, it would be able to predict the right class accordingly.
- Here is the solution :
- Code : https://github.com/ManjiriSDS/The-Spark-Foundation/blob/main/Task%206%20The%20Sparks%20Foundation.ipynb
- Recorded Output in a Video : https://www.youtube.com/watch?v=ubZ2saIFQj0&t=5s
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- Dataset: https://bit.ly/34SRn3b
- Problem Statement : As a sports analysts, find out the most successful teams, players and factors contributing win or loss of a team. Suggest teams or players a company should endorse for its products.
- Here is the solution :
- Dashboard : https://public.tableau.com/profile/sonu75769#!/vizhome/IPLSportEDA/Dashboard1
- Recorded Output in a Video : https://www.youtube.com/watch?v=Gu-IBUyP-CU&t=2s
| Sr No. | Project | Description | Problem Statement | Status |
|---|---|---|---|---|
| BEGINNER | ||||
| 1 | Prediction Using Supervised ML |
|
What will be predicted score if a student studies for 9.25 hrs/ day? | ☑ |
| 2 | Prediction Using Unsupervised ML |
|
From the given 'Iris' dataset, predict the optimal number of clusters and represent it visually. | ☑ |
| INTERMEDIATE | ||||
| 3 | Prediction Using Decision Tree Algorithm |
|
If we feed any new data to this classifier, it would be able to predict the right class accordingly. | ☑ |
| ADVANCED | ||||
| 4 | Exploratory Data Analysis - Sports |
|
As a sports analysts, find out the most successful teams, players and factors contributing win or loss of a team. Suggest teams or players a company should endorse for its products. | ☑ |
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